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Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games

Yulai Zhao · Yuandong Tian · Jason Lee · Simon Du



Policy-based methods with function approximation are widely used for solving two-player zero-sum games with large state and/or action spaces.However, it remains elusive how to obtain optimization and statistical guarantees for such algorithms.We present a new policy optimization algorithm with function approximation and prove that under standard regularity conditions on the Markov game and the function approximation class, our algorithm finds a near-optimal policy within a polynomial number of samples and iterations.To our knowledge, this is the first provably efficient policy optimization algorithm with function approximation that solves two-player zero-sum Markov games.

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